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All Journal MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Jurnal Abdimas BSI: Jurnal Pengabdian Kepada Masyarakat JURNAL MEDIA INFORMATIKA BUDIDARMA JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI JURNAL PENGABDIAN KEPADA MASYARAKAT Jurnal Teknologi Informasi MURA Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Jurnal Teknologi Dan Sistem Informasi Bisnis JLA (Jurnal Lingua Applicata) Jurnal Tekinkom (Teknik Informasi dan Komputer) G-Tech : Jurnal Teknologi Terapan JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Hello World Blend Sains Jurnal Teknik ROUTERS: Jurnal Sistem dan Teknologi Informasi Jurnal Teknologi Informasi Mura Jurnal Bina Informatika dan Komputer (BINER) Jurnal Publikasi Sistem Informasi dan Manajemen Bisnis
Suseno, Akrim Teguh
Institut Teknologi & Sains Nahdlatul Ulama Pekalongan, Indonesia
Agriculture, Biological Sciences & Forestry Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Public Health Social Sciences Other

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Analisis Penjualan Produk Menggunakan Algoritma K-Means dan Apriori Akrim Teguh Suseno
G-Tech: Jurnal Teknologi Terapan Vol 8 No 2 (2024): G-Tech, Vol. 8 No. 2 April 2024
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33379/gtech.v8i2.4255

Abstract

Distro Sextor merupakan industri di bidang pakaian dan fashion yang mengalami penurunan penjualan karena perlambatan ekonomi daerah serta masih belum pulihnya dari dampak pandemi. Oleh sebab itu diperlukan strategi untuk meningkatkan penjualan berbasis teknologi informasi. Penggunaan data mining dengan teknik clustering dan association rule dapat digunakan sebagai analisis penjualan produk di Distro Sextor dengan menemukan pola-pola yang terkait pada transaksi penjualan. Algoritma pada teknik clustering yang digunakan yaitu K-Means dan hasilnya terdapat 5 cluster yaitu 0,1,2,3,4. Cluster yang dipilih adalah cluster 0 karena memiliki jumlah transaksi terbanyak dengan kategori produk yang harganya lebih murah dan jenis produk yang paling sering terjual yaitu kaos, celana pendek, celana panjang, dan lainnya. Analisis ini dilanjutkan dengan teknik association rule yang menggunakan algoritma Apriori untuk menentukan keterkaitan antar produk pada transaksi. Hasilnya terdapat 5 rule dan 7 produk yang dapat direkomendasikan sebagai media promosi yaitu SE130, SE111, SE128, SE126, SE04, SE40, dan SE11.
Penerapan Fitur Seleksi dan Particle Swarm Optimization pada Algoritma Support Vector Machine untuk Analisis Credit Scoring Naufal, Abdul Razak; Suseno, Akrim Teguh
Journal of Computer System and Informatics (JoSYC) Vol 5 No 1 (2023): November 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i1.4409

Abstract

After the Covid-19 pandemic, the banking sector faced significant challenges in contributing to achieving national goals in terms of increasing living standards and equalizing the regional economy. Hundreds of millions of low-income people have no credit or bank accounts because they do not have sufficient credit history to warrant the credit scores assigned to them. An estimated 1.7 billion people (31% of the adult population) do not have an account with a financial institution. People today are usually concentrated in developing countries, especially in China 204 million, India 357 million and Indonesia 102 million people. Because it is very difficult to make accurate predictions in determining credit worthiness for low-income people. Cooperatives are financial institutions that have a crucial role in channeling financing to members and the community to develop their businesses. An inappropriate credit distribution process can have a negative effect on KSP, resulting in frequent losses. This risk is known as problem loans, the cause is the KSP's failure to analyze the credit of prospective debtors. Therefore, calculations are needed to detect opportunities for credit risk default by prospective debtors objectively and precisely so that loan problems do not occur. Credit scoring is a method used to evaluate credit risk in terms of loan applications from consumers [4]. In this research we will provide a solution using classification techniques with feature selection methods in the Particle Swarm Optimization (PSO) Algorithm and Support Vector Machine (SVM) to predict the credit risk of prospective debtors failing to make loan payments. The application of the SVM algorithm in credit scoring research is because SVM is good at data classification. However, the standard SVM model still does not produce optimal results due to the difficulty of determining the best parameters, therefore researchers will optimize it with the Feature Selection and PSO algorithms to determine the best parameters. The results from the combination of several parameters using PSO-SVM obtained an accuracy of 87.23%, therefore the application of this method was proven to improve the performance of the SVM algorithm to increase its accuracy results in predicting the feasibility of granting credit.
Analisis Sentimen Masyarakat Terhadap Isu Migrasi Rohingya Ke Indonesia Kurniasih, Ulfa; Suseno, Akrim Teguh
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 7 No 1 (2025): Januari 2025
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v7i1.1815

Abstract

The phenomenon of the Rohingya refugee exodus is something that attracts a lot of public interest on social media. This study aims to see how the public represents the phenomenon of the presence of Rohingya refugees in Indonesia on X social media with the Decision tree 5.0 algorithm and Naive bayes with the keyword "rohingya". The results of the study showed that negative sentiment is still dominant on X social media with an average of 49.5%, positive sentiment 18.5% and neutral sentiment 27%. For validity testing using the K-Fold Validation method, it shows that the naïve Bayes algorithm has a better level of accuracy with an accuracy level of 83% while the decision tree only has an accuracy level of 78%. The results of the study indicate that Indonesian people through X social media still tend to give a negative attitude towards the presence of Rohingya refugees in Indonesia.
Penerapan Geographic Information System (GIS) Menggunakan Metode GeoJSON untuk Visualisasi Data Geospasial Sebaran UMKM Batik Al'Amin, M; Naufal, Abdul Razak; Suseno, Akrim Teguh
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): January 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.5977

Abstract

Pekalongan City is one of the cities where batik grows. Until now, there are thousands of batiks MSMEs spread across many points in Pekalongan. To introduce batik MSMEs to the general public, a WebGIS-based platform is needed as an information center for Pekalongan batik. This research aims to build a website-based Geographic Information System (GIS) using GeoJSON as a platform for mapping the distribution of MSMEs of Batik and a digital batik literacy center in Pekalongan City. The GeoJSON method is used because it has the ability to read population data in only 14.7 seconds, loading geographic data via the GIS REST API usually takes 38.4 seconds, this makes it very suitable for use in web applications. Its simplicity, compatibility and platform independence have contributed to the geospatial community. GeoJSON is a natural fit with modern web technologies such as JavaScript, which makes it easy to use in web application development and integrates with web service APIs. The test scenario results obtained a total score of 85.8 and the System Usability Scale (SUS) test results obtained a total score of 83.5, which places it in the very good category. The SUS rating scale indicates that potential users find this application easy to use. The design of this application based on test results shows a better effect in terms of ease of use, apart from that, potential users generally give positive marks, so it can be concluded that the implementation of WebGIS using the GeoJSON method for visualizing the distribution of Batik MSMEs can be implemented very well.
Dampak Berita Emas Palsu Terhadap Harga Saham PT Aneka Tambang TBK (ANTM): Analisis dan Prediksi) Ispaniyah, Ispaniyah; Tyas, Putri Cahyaning; Suseno, Akrim Teguh; Wulandari, Umi Meganinditya
ROUTERS: Jurnal Sistem dan Teknologi Informasi Vol. 3 No. 1, Februari 2025
Publisher : Program Studi Teknologi Rekayasa Internet, Politeknik Negeri Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25181/rt.v3i1.3969

Abstract

Fake news or hoaxes, have become a major problem around the world in recent years. This phenomenon not only affects public opinion but can also affect various aspects of socio-economic life, including financial markets. Currently, global stock prices continue to rise and have reached their highest level since 2012-2013. One of the leading mining companies in Indonesia, PT Aneka Tambang Tbk (ANTM), is not entirely dependent on its share price. The impact of fake news on stock prices has become a topic of growing interest in the academic literature. Various previous studies have attempted to identify the relationship between the spread of fake news and stock price fluctuations. Using the RapidMiner application, an analysis of PT ANTM's stock price prediction was conducted using Neural Network (NN) and Linear Regression (LR) algorithms. To assess the accuracy of the prediction, the analysis is performed using the Root Mean Square Error (RMSE) results. The comparative analysis conducted shows that the Neural Network algorithm has a lower error rate of 14,806 +/- 0.000 compared to the Linear Regression algorithm which has a value of 22,379 +/- 0.000. This shows that the Neural Network algorithm has higher accuracy in predicting the share price of PT ANTM. A smaller RMSE value indicates a more accurate prediction. In addition, this study also identified that the time span of the data used (December 19, 2023 - June 19, 2024) can affect the prediction results. Based on the conclusions, the researcher suggests that using a dataset with a longer time span and applying other Deep Learning algorithms to improve prediction accuracy can be used for future research.
Pengaruh Aksi Boikot Terhadap Harga Saham Unilever: Pendekatan Prediktif Dengan Neural Network Dan Linear Regression Yani, Ririn Yuli; Nidaa, Syafiqotun; Suseno, Akrim Teguh; Wulandari, Umi Meganinditya
ROUTERS: Jurnal Sistem dan Teknologi Informasi Vol. 3 No. 1, Februari 2025
Publisher : Program Studi Teknologi Rekayasa Internet, Politeknik Negeri Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25181/rt.v3i1.4009

Abstract

PT Unilever Indonesia Tbk is a  multinational company that produces and markets various consumer goods in various countries to fulfill needs ranging from health, nutrition, daily care and so on. PT Unilever Indonesia Tbk is facing a crisis of calls for a boycott of products due to pro-Israel which has an impact on the Company’s reputation and performance. In the face of this situation, stock price prediction analysis is important to help investors in making decisions. To overcome this problem, this research applies Data Mining Techniques in predicting the share price of PT Unilever Tbk. The two algorithms used are Neural Network and Linear Regression, which are then tested using the Root Mean Squared Error (RMSE) evaluation method. Data processing is done using RapidMiner with historical data period from December 2023 to May 2024. Based on the analysis results, the Linear Regression algorithm produces an RMSE value of 22,745, showing a more accurate prediction compared to the Neural Network algorithm which has an RMSE value of 44,830. The test results show that predicting stock prices using Linear Regression has a lower error rate than the Neural Network. Thus, in this study, the Linear Regression algorithm is superior in predicting the stock price of PT Unilever Indonesia Tbk compared to the Neural Networj. The results of this study are also compared with previous research which shows thaht the accuracy of the stock price prediction model depends on the characteristics of the dataset and the method used. Some previous studies concluded that Neural Network is superior in capturing complex patterns in certain stocks, while Linear Regression is more suitable for data with linear relationships. Therefore, although Linear Regression is better in this study, model selection still needs to be tailored to the characteristics and objectives of the analysis.
Market Basket Analysis Using FP-Growth and Apriori on Distro Store Sales Transaction Wulandari, Umi Meganinditya; Suseno, Akrim Teguh; Kholilurrahman, Muhammad
MATICS: Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Vol 17, No 1 (2025): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/mat.v17i1.28820

Abstract

Market Basket Analysis analyzes consumer buying habits by finding relationships between items in the consumer's shopping basket. This Market Basket Analysis can provide success to the retail industry with the ability to understand consumer behavior and the speed of response to information obtained by retail business owners. This understanding is the result of an analysis that can help business owners improve marketing and sales strategies while utilizing transaction data. Sales transaction data that has been accumulated so far has only become data warehouses, while large amounts of transaction data can bring major changes to the level of competition in business and business actors in order to survive in the business world. In addition, after the COVID-19 outbreak, Indonesia experienced a slowdown in economic growth of 5.31%. This can be overcome by utilizing Market Basket Analysis to increase sales from their businesses. MBA with the methods used are FP-Growth and Apriori to analyze store transaction data in order to obtain association rules that can be used in improving marketing strategies. This analysis was carried out with 3 scenarios for 3 different minimum support values (1%, 2% and 3%) but the same minimum confidence value of 0.6 (60%). The comparison of the two methods is that 2 out of 3 scenarios produce the same association rule, namely 1 final association rule result with a lift value of 1.42. The three scenario results from both methods can be used by business owners as a consideration in determining sales strategies.
IMPLEMENTASI APLIKASI CROWDFUNDING UNTUK PENINGKATAN PENDAPATAN YAYASAN FILANTROPI KABUPATEN BATANG Suseno, Akrim Teguh; Ngizudin, Risal; Hakim, Mujibul
JURNAL PENGABDIAN KEPADA MASYARAKAT Vol 14 No 1 (2024): Juli 2024
Publisher : LPPM UNINUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30999/jpkm.v14i1.2925

Abstract

Yayasan Bangun Pemuda Indonesia (YBPI) merupakan salah satu lembaga filantropi di Kabupaten Batang. Berbagai kegiatan sosial yang telah dilakukan seperti santunan sembako dan uang pokok bulanan kepada anak yatim binaan, rawat sungai dan penghijauan, tahsinul Quran, teras lenggah pantura, dan lain sebagainya.Pemasalahan utama yayasan tersebut adalah kondisi keuangan yayasan yang tidak stabil, hampir setiap bulan dana yang terkumpul dari donasi masyarakat rata-rata hanya 30% dari total dana yang dibutuhkan, 70% sisanya para pengurus yayasan yang berusaha untuk memenuhi. Selain itu metode donasi dari masyarkat masih konvensional yaitu mendatangi para donatur satu persatu maupun whatsapp para donatur lama secara satu persatu. Hal tersebut sangat berdampak pada kegiatan sosial YBPI kepada masyarakat yang menjadi kurang optimal. Melalui program pengabdian masyarakat (PkM) dari ITSNU Pekalongan memberikan pelatihan dan bimbingan terhadap penggunaan aplikasi crowdfunding Kita Bisa yang dapat membantu YBPI dalam meningkatkan pendapatan dari donasi masyarakat di seluruh indonesia secara online. Metode pelaksanan dibagi menjadi 3 yaitu (1) diskusi FGD terkait kebutuhan alat dan sasaran yang akan dicapai, (2) pelatihan dan implementasi aplikasi crowdfunding Kita Bisa, (3) monitoring dan evaluasi terkait hasil dari implementasi aplikasi tersebut. Hasil dari  PkM ini terdapat beberapa donatur baru yang memberikan donasinya dalam salah satu kegiatan yang dicanangkan oleh YBPI dalam waktu kedepan yang telah diverifikasi oleh aplikasi crowdfunding Kita Bisa.
Implementasi Sistem Informasi Point of Sales untuk Koperasi SMP N 1 Bodeh Suseno, Akrim Teguh; Naufal, Abdul Razak; NAWANGNUGRAENI, DEVI ASTRI
Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Vol 6, No 3 (2023): Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstormin
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/japhb.v6i3.5103

Abstract

Kondisi Covid-19 di Indonesia saat ini sudah berkurang drastis sehingga beberapa kantor, sekolah dan tempat umum sudah mulai menjalankan aktivitasnya seperti semula. Dalam hal ini termasuk beberapa sekolah yang telah melaksanakan kegiatan akademik salah satunya SMP N 1 Bodeh yang merupakan salah satu sekolah yang ada di Kecamatan Bodeh Kabupaten Pemalang. SMP N 1 Bodeh memiliki koperasi yang melayani jual beli alat tulis sebagai layanan untuk menunjang kebutuhan siswa dan guru. Namun proses jual beli barang di koperasi tersebut saat ini masih dilakukan secara manual, sehingga proses rekapitalisasi penjualan bulanan akan sulit dilakukan dan membutuhkan waktu yang lama serta rawan kecurangan oleh pengguna. Berdasarkan permasalahan tersebut, dibutuhkan sebuah sistem informasi untuk mengatasi lambatnya proses pelaporan atau rekapitalisasi pada proses jual beli dan meminimalisir kecurangan pada koperasi di SMP N 1 Bodeh. Sistem informasi penjualan point of sales (POS) adalah sistem pencatatan proses jual beli yang digunakan di perusahaan, restoran atau toko retail untuk membantu proses transaksi. Hasil pelaksanaan kegiatan ini menunjukkan bahwa peserta pelatihan guru dan staf sangat setuju dengan implementasi dan pelatihan penggunaan sistem informasi POS. Hasil angket antusias peserta menunjukan sangat setuju 84%, setuju 16%, netral 0%, tidak setuju 0% dan sangat tidak setuju 0%.
Representasi Berita Ekonomi pada Situs DPR RI: Kajian Wacana Korpus Linguistik Kurniasih, Ulfa; Suseno, Akrim Teguh; Muna, Naili
JLA (Jurnal Lingua Applicata) Vol 7, No 1 (2023)
Publisher : DBSMB, Vocational College of Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jla.87635

Abstract

Penelitian ini bertujuan untuk mengeksplorasi fitur linguistik yang digunakan dalam wacana ekonomi yang terdapat dalam berita DPR RI melalui data korpus. Penelitian ini menggunakan metode kuantitatif dan kualitatif sebagai instrumen penelitian. Metode kuantitatif dibutuhkan untuk melihat ciri linguistik serta tema yang tercermin dalam berita. Sementara itu, metode kualitatif dilakukan untuk analisis secara mendalam menggunakan kerangka analisis wacana. Data dalam penelitian ini adalah berita yang terdapat pada laman resmi DPR RI yaitu dpr.go.id yang memiliki kata kunci ekonomi pada judul berita dari Agustus 2022 hingga April 2023. Data yang diambil terdiri atas 100 teks, 22731 tipe, dan 35460 token. Korpus diolah menggunakan perangkat lunak AntConc kemudian dilakukan analisis daftar kata dan korkodans. Analisis daftar kata berdasarkan hasil pengolahan korpus dengan mengambil kata dengan frekuensi tertinggi dari peringkat pertama hingga ke-50. Kata yang dipilih adalah kata selain preposisi atau yang dalam KBBI dilambangkan dengan simbol (p). Pada analisis konkordans digunakan untuk melihat kecenderungan isi teks berita dari kata-kata yang telah mempunyai frekuensi tinggi dan berkaitan antara satu dengan lainnya, yaitu kata ekonomi, indonesia, dpr, pemerintah, pertumbuhan, umkm, masyarakat, dan bank. Hasil penelitian menunjukkan bahwa terdapat lima tema terkait isi berita pada situs resmi DPR RI, yaitu  masalah ekonomi nasional dan global (47%), pemulihan ekonomi (23%), pembiayaan pelaku usaha (5%), peningkatan produk UMKM (8%), dan kebijakan pemerintah yang berdampak langsung ke masyarakat (17%).
Co-Authors Abdul Razak Naufal Achmad Dimyati Afif, Randi Ahmad Zulfan Ary Dwi Anjarini Daroji Daroji Devi Astri Nawangnugraeni Fitroh, Qorry Aina Hakim, Mujibul Ibad, Irsyadul Indra Kurniawan Indra Kurniawan Ispaniyah, Ispaniyah Kholil Mustofa Kholilurrahman, Muhammad Kurniasih, Ulfa Laily Amalia M. Zakki Abdillah Mahardika, Fajar Merani, Sania Galuh Milzam, Muhammad Mohammad AL AMIN Mohammad Reza Maulana, Mohammad Reza Muna, Naili NAWANGNUGRAENI, DEVI ASTRI Nidaa, Syafiqotun Risal Ngizudin Tyas, Putri Cahyaning Ulfa Kurniasih Ulfah, Sofia Wulandari, Umi Meganinditya Yani, Ririn Yuli